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<a id='AffineInvariantMCMC.jl-1'></a> | ||
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# AffineInvariantMCMC.jl | ||
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Module AffineInvariantMCMC.jl provides functions for Bayesian sampling using Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler (aka Emcee) based on a paper by Goodman & Weare, "Ensemble samplers with affine invariance" Communications in Applied Mathematics and Computational Science, DOI: [10.2140/camcos.2010.5.65](http://dx.doi.org/10.2140/camcos.2010.5.65), 2010. | ||
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AffineInvariantMCMC.jl module functions: | ||
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<a id='AffineInvariantMCMC.flattenmcmcarray-Tuple{Array,Array}' href='#AffineInvariantMCMC.flattenmcmcarray-Tuple{Array,Array}'>#</a> | ||
**`AffineInvariantMCMC.flattenmcmcarray`** — *Method*. | ||
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Flatten MCMC arrays | ||
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<a target='_blank' href='https://github.com/madsjulia/AffineInvariantMCMC.jl/tree/104df63a5b9de2991793e1f99fc117f037b72357/src/AffineInvariantMCMC.jl#L96' class='documenter-source'>source</a><br> | ||
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<a id='AffineInvariantMCMC.sample' href='#AffineInvariantMCMC.sample'>#</a> | ||
**`AffineInvariantMCMC.sample`** — *Function*. | ||
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Bayesian sampling using Goodman & Weare's Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler (aka Emcee) | ||
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``` | ||
AffineInvariantMCMC.sample(llhood, numwalkers=10, numsamples_perwalker=100, thinning=1) | ||
``` | ||
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Arguments: | ||
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* `llhood` : function estimating loglikelihood (for example, generated using Mads.makearrayloglikelihood()) | ||
* `numwalkers` : number of walkers | ||
* `x0` : normalized initial parameters (matrix of size (length(params), numwalkers)) | ||
* `thinning` : removal of any `thinning` realization | ||
* `a` : | ||
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Returns: | ||
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* `mcmcchain` : final MCMC chain | ||
* `llhoodvals` : log likelihoods of the final samples in the chain | ||
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Reference: | ||
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Goodman & Weare, "Ensemble samplers with affine invariance", Communications in Applied Mathematics and Computational Science, DOI: 10.2140/camcos.2010.5.65, 2010. | ||
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<a target='_blank' href='https://github.com/madsjulia/AffineInvariantMCMC.jl/tree/104df63a5b9de2991793e1f99fc117f037b72357/src/AffineInvariantMCMC.jl#L36-L59' class='documenter-source'>source</a><br> | ||
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<a id='BIGUQ.jl-1'></a> | ||
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# BIGUQ.jl | ||
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Module BIGUQ provides advanced techniques for Uncertainty Quantification, Experimental Design and Decision Analysis based on Bayesian Information Gap Decision Theory (BIGDT). | ||
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References: | ||
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* O’Malley, D., Vesselinov, V.V., A combined probabilistic/non-probabilistic decision analysis for contaminant remediation, Journal on Uncertainty Quantification, SIAM/ASA, 10.1137/140965132, 2014. | ||
* O’Malley, D., Vesselinov, V.V., Bayesian-Information-Gap decision theory with an application to CO2 sequestration, Water Resources Research, 10.1002/2015WR017413, 2015. | ||
* Grasinger, M., O'Malley, D., Vesselinov, V.V., Karra, S., Decision Analysis for Robust CO2 Injection: Application of Bayesian-Information-Gap Decision Theory, International Journal of Greenhouse Gas Control, 10.1016/j.ijggc.2016.02.017, 2016. | ||
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Relevant examples: | ||
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* [Information Gap Analysis](http://madsjulia.github.io/Mads.jl/Examples/infogap) | ||
* [Decision Analysis](http://madsjulia.github.io/Mads.jl/Examples/bigdt/source_termination) | ||
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BIGUQ.jl module functions: | ||
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<a id='BIGUQ.getmcmcchain-Tuple{BIGUQ.BigDT,Any}' href='#BIGUQ.getmcmcchain-Tuple{BIGUQ.BigDT,Any}'>#</a> | ||
**`BIGUQ.getmcmcchain`** — *Method*. | ||
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Get MCMC chain | ||
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<a target='_blank' href='https://github.com/madsjulia/BIGUQ.jl/tree/770772d2a5e0c0945e40430564fa787fe9b47398/src/BIGDT.jl#L22' class='documenter-source'>source</a><br> | ||
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<a id='BIGUQ.makebigdts-Tuple{BIGUQ.BigOED,Any,Any}' href='#BIGUQ.makebigdts-Tuple{BIGUQ.BigOED,Any,Any}'>#</a> | ||
**`BIGUQ.makebigdts`** — *Method*. | ||
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Make BIGDT analyses for each possible decision assuming that the proposed observations `proposedobs` are observed | ||
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<a target='_blank' href='https://github.com/madsjulia/BIGUQ.jl/tree/770772d2a5e0c0945e40430564fa787fe9b47398/src/BIGOED.jl#L50' class='documenter-source'>source</a><br> | ||
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<a id='BIGUQ.makebigdts-Tuple{BIGUQ.BigOED}' href='#BIGUQ.makebigdts-Tuple{BIGUQ.BigOED}'>#</a> | ||
**`BIGUQ.makebigdts`** — *Method*. | ||
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Makes BIGDT analyses for each possible decision assuming that no more observations will be made | ||
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<a target='_blank' href='https://github.com/madsjulia/BIGUQ.jl/tree/770772d2a5e0c0945e40430564fa787fe9b47398/src/BIGOED.jl#L23' class='documenter-source'>source</a><br> | ||
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<a id='BIGUQ.BigDT' href='#BIGUQ.BigDT'>#</a> | ||
**`BIGUQ.BigDT`** — *Type*. | ||
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BigOED type | ||
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<a target='_blank' href='https://github.com/madsjulia/BIGUQ.jl/tree/770772d2a5e0c0945e40430564fa787fe9b47398/src/BIGDT.jl#L2' class='documenter-source'>source</a><br> | ||
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<a id='BIGUQ.BigOED' href='#BIGUQ.BigOED'>#</a> | ||
**`BIGUQ.BigOED`** — *Type*. | ||
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BigOED type | ||
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<a target='_blank' href='https://github.com/madsjulia/BIGUQ.jl/tree/770772d2a5e0c0945e40430564fa787fe9b47398/src/BIGOED.jl#L1' class='documenter-source'>source</a><br> | ||
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<a id='DocumentFunction.jl-1'></a> | ||
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# DocumentFunction.jl | ||
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Module provides tools for documenting Julia functions providing information about function methods, arguments and keywords. | ||
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DocumentFunction.jl module functions: | ||
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<a id='DocumentFunction.documentfunction' href='#DocumentFunction.documentfunction'>#</a> | ||
**`DocumentFunction.documentfunction`** — *Function*. | ||
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Create function documentation | ||
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Arguments: | ||
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* `f`: function to be documented" | ||
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Keywords: | ||
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* `maintext`: function description | ||
* `argtext`: dictionary with text for each argument | ||
* `keytext`: dictionary with text for each keyword | ||
* `location`: show/hide function location on the disk | ||
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<a target='_blank' href='https://github.com/madsjulia/DocumentFunction.jl/tree/d3db0c920e37b46a8ebac13fa09aa0f961f18399/src/DocumentFunction.jl#L109-L122' class='documenter-source'>source</a><br> | ||
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<a id='DocumentFunction.getfunctionarguments' href='#DocumentFunction.getfunctionarguments'>#</a> | ||
**`DocumentFunction.getfunctionarguments`** — *Function*. | ||
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Get function arguments | ||
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Arguments: | ||
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* `f`: function to be documented" | ||
* `m`: function methods | ||
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<a target='_blank' href='https://github.com/madsjulia/DocumentFunction.jl/tree/d3db0c920e37b46a8ebac13fa09aa0f961f18399/src/DocumentFunction.jl#L147-L154' class='documenter-source'>source</a><br> | ||
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<a id='DocumentFunction.getfunctionkeywords' href='#DocumentFunction.getfunctionkeywords'>#</a> | ||
**`DocumentFunction.getfunctionkeywords`** — *Function*. | ||
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Get function keywords | ||
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Arguments: | ||
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* `f`: function to be documented | ||
* `m`: function methods | ||
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<a target='_blank' href='https://github.com/madsjulia/DocumentFunction.jl/tree/d3db0c920e37b46a8ebac13fa09aa0f961f18399/src/DocumentFunction.jl#L177-L184' class='documenter-source'>source</a><br> | ||
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<a id='DocumentFunction.getfunctionmethods-Tuple{Function}' href='#DocumentFunction.getfunctionmethods-Tuple{Function}'>#</a> | ||
**`DocumentFunction.getfunctionmethods`** — *Method*. | ||
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Get function methods | ||
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Arguments: | ||
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* `f`: function to be documented | ||
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Return: | ||
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* array with function methods | ||
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<a target='_blank' href='https://github.com/madsjulia/DocumentFunction.jl/tree/d3db0c920e37b46a8ebac13fa09aa0f961f18399/src/DocumentFunction.jl#L37-L47' class='documenter-source'>source</a><br> | ||
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<a id='DocumentFunction.stdoutcaptureoff-Tuple{}' href='#DocumentFunction.stdoutcaptureoff-Tuple{}'>#</a> | ||
**`DocumentFunction.stdoutcaptureoff`** — *Method*. | ||
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Restore STDOUT | ||
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<a target='_blank' href='https://github.com/madsjulia/DocumentFunction.jl/tree/d3db0c920e37b46a8ebac13fa09aa0f961f18399/src/DocumentFunction.jl#L26-L28' class='documenter-source'>source</a><br> | ||
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<a id='DocumentFunction.stdoutcaptureon-Tuple{}' href='#DocumentFunction.stdoutcaptureon-Tuple{}'>#</a> | ||
**`DocumentFunction.stdoutcaptureon`** — *Method*. | ||
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Redirect STDOUT to a reader | ||
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<a target='_blank' href='https://github.com/madsjulia/DocumentFunction.jl/tree/d3db0c920e37b46a8ebac13fa09aa0f961f18399/src/DocumentFunction.jl#L15-L17' class='documenter-source'>source</a><br> | ||
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